Teresa Scherzer, PhD, MSW, Academic Programs Evaluator, Office of the Dean , School of Nursing on strategies to assess unconscious bias. (Transcript)

For many years, scientists have been working on instruments to assess unconscious bias (also known as implicit associations). Of the various tools that are available, the Implicit Association Test (IAT) is one of the most popular and well-studies. The IAT was developed as part of a project to detect unconscious bias based on several factors including race, gender, sexual orientation and national origin. It was developed as part of Project Implicit, which blends basic research and educational outreach in a virtual laboratory that allows users to exam one’s own hidden biases and understand stereotypes that exist below one’s conscious awareness. Project Implicit comprises a network of laboratories, technicians, and research scientists at Harvard University, the University of Washington and the University of Virginia.

How does the IAT work?

The IAT measures the relative strength of associations between pairs of concepts. It is designed as a sorting task in which individuals are asked to sort images or words that appear on a computer screen into one of two categories. The basic premise is that when two concepts are highly correlated, people are able to pair those concepts more quickly than two concepts that are not well associated. The IAT is relatively resistant to social desirability concern, and the reliability and validity have been rigorously tested.

How is the IAT used?

The IAT is powerful instrument, which has been used to explore the impact of unconscious bias on behavior. Here are some examples highlighting the use of the IAT in healthcare.
  • A greater pro-White bias (measured using the IAT) among physicians resulted in an increased likelihood of prescribing thrombolytics for White patients compared to Blacks presenting with acute coronary syndrome (Green, 2007).
  • A greater pro-White bias (measured using the IAT) was associated with a greater inclination to prescribe pain medications for White versus Black children (Sabin, 2012).
  • Greater pro-White bias (measured using the IAT) was associated with poorer ratings of interpersonal care among Black patients (Cooper, 2012).